{"id":"W1601572767","doi":"10.5220/0002997600880093","title":"BREAST MASS DETECTION USING BILATERAL FILTER AND MEAN SHIFT BASED CLUSTERING","year":2010,"lang":"en","type":"article","venue":"","topic":"AI in cancer detection","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Artificial intelligence; Computer science; Pattern recognition (psychology); Cluster analysis; Mammography; Image segmentation; Mean-shift; Filter (signal processing); Pixel; Segmentation; Computer vision; Feature extraction; Artificial neural network; Feature vector; Breast cancer; Cancer","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007836053,0.0004278708,0.0007067915,0.001765824,0.0004548897,0.0006097703,0.0007297662,0.00113242,0.00112785],"category_scores_gemma":[0.001698386,0.0003722977,0.0008167436,0.001128943,0.0004489008,0.000984982,0.0005584725,0.0003480169,0.000495195],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007729292,"about_ca_system_score_gemma":0.0006784846,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004766759,"about_ca_topic_score_gemma":0.005398498,"domain_scores_codex":[0.9991593,0.00009887756,0.00003229026,0.0001514895,0.0005050365,0.00005304814],"domain_scores_gemma":[0.9996089,0.0001270956,0.00005112606,0.00003933084,0.0001541293,0.00001951189],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003007017,0.00009574172,0.00271357,0.0001675797,0.0001649439,0.0001398138,0.0001888258,0.04566474,0.1596028,0.005393197,0.001761332,0.7838067],"study_design_scores_gemma":[0.00004383935,0.0001639508,0.00677526,0.00002006679,0.0000989287,0.0006253822,0.00005485163,0.9133248,0.06487361,0.005434467,0.008506313,0.00007846081],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03191832,0.0005721417,0.9651935,0.000141875,0.00005194463,0.00004996881,0.00004125324,0.0007052706,0.001325632],"genre_scores_gemma":[0.2607602,0.0006369583,0.7342595,0.0001040459,0.00009577059,0.00009642686,0.0001232747,0.0001055878,0.003818277],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004766759,"threshold_uncertainty_score":0.009478033,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01617928983089667,"score_gpt":0.2316211926397184,"score_spread":0.2154419028088217,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}